Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2023. This dataset is a prime candidate for conducting analyses on salary disparities, the relationship between department/division and salary, and the distribution of salaries across gender and grade levels.

Statistical models can be applied to predict base salaries based on factors such as department, grade, and length of service. Machine learning techniques could also be employed to identify patterns and anomalies in the salary data, such as outliers or instances of significant inequity.

Some analysis to be performed with this dataset can include:

• Gender Pay Gap Analysis: An examination of salary differences between genders within similar roles, grades, and departments to identify any disparities that need to be addressed.
• Departmental Salary Analysis: Analyzing the distribution of salaries across different departments and divisions to understand how compensation varies within the organization.
• Impact of Overtime and Longevity Pay: Evaluating how overtime and longevity pay contribute to the overall compensation of employees and identifying trends or patterns in these payments. ​

The average annual salary of a Data Architect in India was estimated to be over *********** Indian rupees per annum, the highest among other jobs in the Data Science sector in India. It was followed by data Scientist and Database Developer roles.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for master in business administration business statistics data analytics in the U.S.

Context

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Structure of the Dataset

https://i.imgur.com/G8GwKx5.png" alt="">

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Cover Photo by rupixen.com on Unsplash

The motivation behind analyzing salary data is to gain insights into compensation trends, identify factors that influence pay, and understand disparities across industries, locations, or job roles. For businesses, this analysis is crucial in shaping competitive compensation packages, attracting top talent, and ensuring fair pay practices. Additionally, individuals can benefit from understanding how their salaries compare to industry standards, aiding in negotiation strategies.

Context With increasing attention on pay transparency and equity, salary data has become a critical dataset for human resources departments, economists, and policymakers. Companies and industries alike need to assess compensation against benchmarks, inflation, and the evolving job market. Salary datasets often contain variables such as job titles, experience levels, education, locations, and industries, which are essential in determining pay structures. This analysis allows for a deeper dive into trends like gender pay gaps, regional disparities, and the impact of education or experience on earnings.

For the Kaggle community, salary datasets provide rich opportunities for performing exploratory data analysis, statistical modeling, and predictive analytics. It serves as a hands-on opportunity to practice data wrangling, feature engineering, and model building, especially in the realm of HR analytics.

Description This CSV file contains anonymized company salary data across various industries, roles, and locations. The dataset includes key variables such as:

Job Title: The role of the employee (e.g., Data Analyst, Software Engineer). Years of Experience: Number of years the employee has been in the workforce or industry. Education Level: The highest degree obtained by the employee (e.g., Bachelor's, Master's). Location: City or country where the employee works. Industry: The sector in which the company operates (e.g., Finance, Technology). Annual Salary: The employee’s yearly earnings, including bonuses or incentives. Gender: Gender identification of the employee (if available). Remote Work Percentage: The percentage of work conducted remotely, which may influence salary based on location independence. The dataset is perfect for understanding how salaries vary by job role, region, industry, and experience level. It can also be used to uncover trends such as salary growth over time, the impact of education or certifications on compensation, or potential gender pay gaps. Through data visualization, predictive models, and regression analysis, users can extract meaningful insights that could inform corporate strategy, HR policies, or even career decisions.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for business administration statistics and data analytics in the U.S.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset contains information of 22700+ Software Professionals with different features like their Salaries (₹), Name of the Company, Company Rating, Number of times Salaries Reported, and Location of the Company.

Extra Features Added: 1. Employment Status 2. Job Roles

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Roles Included:

Android Developer Android Developer - Intern Android Developer - Contractor Android Developer Contractor Senior Android Developer Android Software Engineer Android Engineer Android Applications Developer - Intern Android Applications Developer Android App Developer - Intern Senior Android Developer and Team Lead Android Tech Lead Product Engineer (Android) Software Engineer - Android Android Software Developer Android Software Developer - Intern Senior Android Developer Contractor Junior Android Developer - Intern Junior Android Developer Android Applications Developer - Contractor Android App Developer Lead Android Developer Android Engineer - Intern Sr. Android Developer Senior Android Engineer Senior Software Engineer - Android Android - Intern Android Android & Flutter Developer - Intern Associate Android Developer Senior Android Applications Developer Android Developer Trainee Sr Android developer Android Trainee Android Trainee - Intern Trainee Android Developer Android Lead Android Lead Developer Android Development - Intern Android Development Android Team Lead Senior, Android Developer Lead Android Engineer Tech Lead- Android Applications Developer Senior Android Software Developer Full Stack Android Developer Android Framework Developer Android Architect Android & Flutter Developer Senior Software Engineer, Android Android App Development Sr Android Engineer Android Team Leader Android Technical Lead SDE2(Android) Web Developer/Android Developer - Intern Android Applications Develpoers Android Platform Developer - Intern Android Test Engineer Senior Engineer - Android Android Framework Engineer Game Developer ( Android, Windows) Android Testing Senior Software Engineer (Android/Mobility) Ace - Android Development Software Developer (Android) - Intern Android Mobile Developer Android and Flutt...

This statistic displays the programmer analyst salary in Italy in 2016, broken down by gender. According to data, the highest salary was among male programmer analyst who are also managers, with an average income of ****** euros per year. Female employed programmer analyst earned an average of ****** euros per year, ** euros less of their male correspondents.

This dataset provides detailed information about the salaries and various attributes of data professionals from 2009 to 2016. It is designed to help understand the salary trends and other related factors in the data profession over this period. The dataset consists of two files, each containing specific details about data professionals, including personal information, job details, and performance metrics.

Salary Prediction of Data Professions.csv - Original File

FIRST NAME: First name of the data professional (String)

LAST NAME: Last name of the data professional (String)

SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)

DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)

CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)

DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)

AGE: Age of the data professional (Integer)

SALARY: Annual salary of the data professional (Float)

UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)

LEAVES USED: Number of leaves used by the data professional (Integer)

LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)

RATINGS: Performance ratings of the data professional (Float)

PAST EXP: Past work experience in years before joining the current company (Float)

cleaned_data.csv - Processed File

FIRST NAME: First name of the data professional (String)

LAST NAME: Last name of the data professional (String)

SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)

DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)

CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)

DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)

AGE: Age of the data professional (Integer)

SALARY: Annual salary of the data professional (Float)

UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)

LEAVES USED: Number of leaves used by the data professional (Integer)

LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)

RATINGS: Performance ratings of the data professional (Float)

PAST EXP: Past work experience in years before joining the current company (Float)

DAY: Day of the current date (Integer)

MONTH: Month of the current date (Integer)

YEAR: Year of the current date (Integer)

Usage:

This dataset is valuable for researchers, analysts, and data enthusiasts who want to explore and analyze salary trends in the data profession. It can be used to build predictive models, perform statistical analysis, and gain insights into how different factors such as gender, age, experience, and performance ratings affect salaries in the data industry.

Source:

This data was sourced from Kaggle - Salary Prediction of Data Professions. It has been cleaned and prepared for analysis.

License:

Please refer to the original dataset on Kaggle for licensing details

As of 2022, the median annual salary of a data analyst in the Chinese data and artificial intelligence industry reached ** thousand yuan. According to the source, junior-level employees in the technology industry gained the most from changing their jobs. In contrast, from the middle-level upwards, the salary increases are much lower after taking a position at a new employer.

This statistic displays the programmer analyst salary in Italy in 2016, broken down by company size and professional category. According to data, the highest salary was among large companies, with an average of ****** euros per year for a manager, followed by medium-sized companies (****** euros).

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for data analytics business statistics in the U.S.

This statistic displays the programmer analyst salary in Italy in 2016, broken down by professional seniority and category. According to data, the highest salary was among those who had worked for more than five years, with an average of ****** euros per year for managers, followed by those who had worked between 3 and 5 years (****** euros).

TeamStation AI System Report on LATAM IT Salaries 2024 A Comprehensive Analysis of Salary Trends in Latin America’s IT Sector

Introduction The 2024 TeamStation AI Salary Report provides a comprehensive analysis of IT salary structures in 19 Latin American countries, offering scientific insights into compensation trends across various job roles, experience levels, and contract types. This report leverages 1,521 salary records collected from real hiring data, offering the most precise, non-biased compensation insights in the region​

Key Findings 1. Salary Breakdown by Country Three countries lead in IT talent representation:

🇲🇽 Mexico 🇨🇴 Colombia 🇦🇷 Argentina Brazil and Chile also emerge as key players, showcasing robust demand for high-level AI, ML, and DevOps professionals. Meanwhile, Uruguay and Costa Rica provide a cost-effective alternative for high-skilled developers​

1. Role-Based Salary Trends The six most common roles in the LATAM IT market are:

Full-Stack Developer Front-End Developer Back-End Developer App Developer DevOps Engineer Data Engineer Additionally, AI, MLOps, and Cloud Engineers are seeing increasing demand, commanding salaries up to 60% higher than other IT positions​

1. Salary Ranges by Experience Level Salaries vary significantly based on seniority and contract type:

Junior Developers: $10,000 – $30,000 per year Mid-Level Developers: $20,000 – $50,000 per year Senior Developers: $25,000 – $100,000 per year (with some AI engineers exceeding this range) Full-time contracts pay the highest salaries, while freelance engagements have lower total compensation, but can reach premium rates for niche AI/ML expertise​

1. Key Statistical Insights Average salary across all roles: $30,470.02 USD Standard deviation: $56,817.32 USD (showing large variances based on expertise and role) Minimum salary recorded: $500 USD Maximum salary recorded: $800,000 USD Salary percentiles: 25th percentile: $7,000 USD 50th percentile (median): $16,300 USD 75th percentile: $36,000 USD These figures indicate a wide salary stratification, especially for senior roles and AI-related positions​ .

2. Contract Type & Compensation Salaries vary based on contract type:

Full-time developers earn higher base salaries with benefits. Freelancers earn lower annual salaries but some charge premium hourly rates in AI, Cloud, and DevOps. Mid and senior-level engineers prefer full-time contracts for higher pay and stability​ . Regional Salary Insights Highest-paying regions: 🇨🇱 Chile, 🇧🇷 Brazil, 🇲🇽 Mexico. Mid-range salaries: 🇨🇴 Colombia, 🇦🇷 Argentina. Cost-effective hiring: 🇺🇾 Uruguay, 🇨🇷 Costa Rica​ . Strategic Takeaways AI & MLOps engineers are the most expensive to hire in Mexico, Brazil, and Chile. Cloud, DevOps, and AI roles are seeing the fastest growth in salary demand. Best locations for cost-effective hiring: Colombia, Argentina, Uruguay. AI-driven hiring platforms like TeamStation AI reduce time-to-hire and salary mismatches​

Comprehensive H1B visa data including salary information, approval rates, company sponsorship data, and immigration statistics.

Salary by Profession and Country Over Time

Salary Differences by Country and Profession

By Kelly Garrett [source]

About this dataset

This dataset contains survey responses from 882 data professionals from 46 countries who took part in the 2021 Global Data Professional Salary Survey. Our goal was to understand how much database administrators, data analysts, data architects, developers and data scientists make across the world in 2017-2021.

The survey covers three years of salary trends, allowing you to compare and contrast movements over time. It also includes an optional postal code field which can be used to identify global regions with specific salary trends. In addition, all questions asked this year were also asked in 2017 and 2018 so that you can easily track changes in compensation over three years.

The spreadsheet contains anonymized responses which are provided as public domain making it available for any purpose without attribution or mention of anyone else. With this dataset at your disposal you'll have access to the detailed salary information needed to make informed decisions about your career development!

More Datasets

For more datasets, click here.

Featured Notebooks

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How to use the dataset

• Start by familiarizing yourself with the columns in this dataset. The columns range from age of respondent to country of residence. It also includes salary information for each year (average annual income for 2017, 2018, and 2019). Read through each column header carefully to understand what you're looking at.

• Explore some basic summary statistics about the sample group such as median salary levels by profession or average age by nationality are interesting ways to get acquainted with this data set quickly. Excel's native statistical tools may be used here if you're using an excel file version as your source material; otherwise, you can use any programming language or statistics software that supports importing an exportable CSV (Comma Separated Values) format file or conversion thereof into something manipulable form like a spreadsheet or table structure within your preferred platform..

• You'll then want to identify which factors might be influencing salaries such as experience level, gender and geographical location etc., and attempt some correlation testing between those features against salaries across different job roles or countries over time - where possible without having external datasets available terms of area data points matching up perfectly between thematic dimensions presented within the Respondents' Survey Results tab.. Subsets may also prove relevant when carrying out deeper statistical testing—for example isolating particular participation sets like Ireland alone versus looking at just Europe/Middle East/Africa region altogether..

• Finally look at how these factors have changed over time - it's worth bearing in mind that seasonality might play a role here too depending on where respondents originally reside so it could still be relevant if larger trends towards comparing yearly cohorts differs more widely than expected based purely national economic condition context changes during particular quarters throughout those periods tracked in our findings report � comparison purposes if looking country-by-country instead just individual profiles without taking overall stimulant effects into account e.g higher education qualifications among ~2 yr cohorts vs ~3 yr ones across different populations: Comparing annual amounts doled out employers making ultra-quick transitioning easier tracking changes alone isn't feasible because they're normalized

Research Ideas

• Analyzing regional salary gaps amongst data professionals within the same country, or between countries.
• Evaluating trends in salary rates over time by reviewing changes in year over year responses.
• Generating employer profiles by comparing the salary range of employees at different organizations and industries, as well storing demographic info of individuals who participated in the survey (i.e age range, gender etc)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2019_Data_Professional_Salary_Survey_Responses.csv

File: Data_Professional_Salary_Survey_Responses.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Kelly Garrett.

In 2024, the expected median starting salary for MBA graduates worldwide was ******* U.S. dollars. On the other hand, master's graduates in data analytics, business analytics, finance, and management were expected to have a median salary of ****** U.S. dollars.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for engineering data analytics and statistics in the U.S.

This data was collected by the team https://dou.ua/ . This resource is very popular in Ukraine. It provides salary statistics, shows current vacancies and publishes useful articles related to the life of an IT specialist. This dataset was taken from the public repository https://github.com/devua/csv/tree/master/salaries . This dataset will include the following data for each of the specialist: salary, position (f.e. DevOps Engineer, 3D Artist, Data Scientist, Project Manager), experience, city. I think this dataset will be useful to our community. Thank you.